The present invention generally relates to computer vision. The present invention has specific application for face recognition.
The increasing interest in the face recognition field is fueled by its many potential applications as well as for its scientific interest. Recent work on subspace analysis techniques for face recognition has proven to be very useful under certain “constrained” circumstances (e.g. where the illumination conditions remain closely the same for learning and testing shots, where facial expressions between learning and testing do not differ much, etc.). Among them, it is worthwhile to emphasize the Principal Component Analysis (PCA) approach.
Unfortunately, many problems remain to be solved before the PCA approach can be applied to “unconstrained” domains. Two of these problems are: (i) the imprecise localization of faces and (ii) occlusions. Each of these problems implies a failure of identification in known face recognition approaches. Almost nothing has been proposed to overcome the first problem and little has been done to overcome the second problem.
Accordingly, new face recognition approaches are needed. There is a need to provide a face recognition approach that recognizes a face where the localization step does not guarantee a precise localization and/or where parts of the face appear occluded by, for example, glasses or clothing.